The invention relates generally to the estimation of current position, velocity and acceleration state vectors and associated uncertainty estimation (covariance) of a boosting ballistic missile using passive infrared angles-only measurements from multiple satellite platforms. In particular, the invention relates to constructing and updating a state vectors and covariance matrices.
Existing legacy methods for Overhead Persistent Infra-Red (OPIR) processing use the measurements to do a least-squares fit to either (1) missile booster parameters, or (2) a polynomial curve. The OPIR Robust Boost-Phase State Estimation Algorithm (ORBSEA) process was developed to investigate Kalman Filter techniques to process the OPIR measurements absent information about the target missile parameters. The exemplary Kalman Filter performs adequately for boosting and ballistic trajectories. This Kalman Filter adjusts quickly to varying dynamics over the course of the trajectory, including transition from high thrusting to booster burnout. Another expected advantage of this Kalman Filter is the accurate estimation of the State Vector errors (via covariance). The covariance includes the uncertainty in the OPIR measurements and the uncertainty in the missile dynamics.